Recently quantitative imaging has become a very important topic for pharmaceutical companies. Drug development is very costly and lengthy in time and often different phases of clinical trials are necessary in order to validate the effectiveness of a drug. Presently, pharmaceutical companies are developing agents that, when injected into human subjects, can be imaged, at the functional and anatomical levels to quantify the effectiveness of a drug. This type of agent can significantly reduce the length of clinical trial and the cost of drug development.
Accordingly, medical image analysis vendors are developing dedicated systems and tools to analyze and quantify such clinical trial images. For different image modality, such as CT, MR, PET, SPECT, different dedicated analysis tools may need to be developed. Utilizing present methods, analysis is application-centered. That is, the interested user first starts the application and then loads the data that is intended for the application. The respective clinical trial center may therefore need to have installed multiple applications for different clinical trials. It is therefore inefficient for users to look for, and locate, the right application and load the right image intended for analysis. Furthermore, it is not uncommon that two analysis software applications from two different vendors can do the same analysis. However, the analysis results may differ due to variations in the vendor platforms. For example, some critical parameters, such as the CT Hounsfield unit value for thresholding, may be hard coded in each application and vary across platforms. There is presently, no standardized way to minimize these differences and their impact among analysis results.
It is therefore highly desirable to have a method to organize the applications in an image-centered way, so that images and applications can not be mismatched. Furthermore, it is also highly desirable to systematically reduce or eliminate the analysis result difference encountered by different vendors.